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Understanding an Acquisition Function Family for Bayesian Optimization

2023/10/16 by Kong, Jiajie, Pourmohamad, Tony, Lee, Herbert K. H.
#Computation (stat.CO) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2310.10614

Abstract

Bayesian optimization (BO) developed as an approach for the efficient optimization of expensive black-box functions without gradient information. A typical BO paper introduces a new approach and compares it to some alternatives on simulated and possibly real examples to show its efficacy. Yet on a different example, this new algorithm might not be as effective as the alternatives. This paper looks at a broader family of approaches to explain the strengths and weaknesses of algorithms in the family, with guidance on what choices might work best on different classes of problems.

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